Option framework for managing on demand service offerings

ABSTRACT

A method of and system for managing on-demand service offerings in a service delivery chain. The method comprises the steps of a service provider announcing upfront capacity pricing, an on-demand premium structure, and an on-demand exercise structure; a service distributor committing to upfront capacity and to units of on-demand options; and the service provider provisioning a number of resources to the collection of service distributors. Preferably, the upfront capacity pricing includes three components. A first component is a price structure for capacity or resources to be purchased for immediate use, a second component is an on-demand premium structure, and a third component is an on-demand usage fee structure.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention generally relates to on-demand business processes, andmore specifically, to an option framework for managing on-demand serviceofferings.

2. Background Art

In an increasingly volatile business environment characterized byintense global competition, short product life cycles, increasedtechnological innovation and complexity, and time sensitive customerdemand, the focus of competition in global markets is increasinglyshifting from cost, quality and service to speed, flexibility andinnovation.

Customers want flexibility in technology investment decisions. This needfor flexibility is a logical reaction to risks resulting from demanduncertainty, technology evolution and market fluctuation. These factorsof risks can significantly influence the decision-making processes ofboth the customers and the providers.

E-business “On-Demand” that is promoted recently by several providers inthe Information Technology (IT) industry is a new way of deploying ITinfrastructure and offering IT services. It was developed in response tothe need to hedge the aforementioned risks and react to fluctuatingmarket conditions in real time. Customer's may pay for and consumeresources on an as needed basis. This provides customer flexibility, amechanism for risk sharing and a means to enhance information flows.

However, “On-Demand” offerings introduce uncertainties of its own forboth the service provider and customer. The following are a subset ofthose management concerns: What are the impacts of “On-Demand” to boththe suppliers and buyers' investment and purchase behaviors forIT/Technology/E-business? What are the implications of “On-Demand”offerings to suppliers and buyers' revenue, cost, and/or profit? What isthe value of on-demand to a specific buyer? When should a buyer use thetraditional buy-and-operate mode and when should he invoke “On-Demand”?Where (i.e., in which markets/industries/sectors) should a suppliertarget “On-Demand” offerings? How should a supplier price the“On-Demand” (with respect to the traditional buy-and-operate model)? Howshould a supplier design, provision, and manage the “On-Demand” productsand services?

The existing management techniques and practices for “On-Demand” do notanswer these questions adequately. They do not handle demand risk,technology risks and market risks.

SUMMARY OF THE INVENTION

An object of this invention is to improve management techniques andpractices for on-demand service offerings.

Another object of the present invention is to apply risk managementtechniques to develop business insights to help suppliers or buyers foron-demand decisions.

A further object of the invention is to codify a decision-makingframework to guide suppliers (or buyers) in making a broad range ofon-demand decisions.

Another object of the invention is to provide an option framework toanalyze “On-Demand.”

An object of the preferred embodiment of the present invention is toprovide models and tools to evaluate the value of “on-Demand” to bothservice providers and buyers, by quantifying the impact of on-demand onprofits, costs and revenues.

These and other objectives are attained with a method of and system formanaging on-demand service offerings in a service delivery chain,wherein a service provider provides resources to a collection of servicedistributors, and said service distributors distribute said resources toend users. The method comprises the steps of the service providerannouncing upfront capacity pricing, an on-demand premium structure, andan on-demand exercise structure; at least one of the servicedistributors committing to upfront capacity and to units of on-demandoptions; and the service provider provisioning a number of resources tothe collection of service distributors.

In the preferred embodiment, the upfront capacity pricing includes threecomponents. A first component is a price structure for capacity orresources to be purchased for immediate use, a second component is anon-demand premium structure, and a third component is an on-demand usagefee structure.

This invention is a response to the shortcomings of prior techniques tomanage “On-Demand” services and is motivated by the need for a unifieddecision framework that incorporates demand, technology and marketrisks. The invention presents a novel application of financial riskmanagement techniques to “On-Demand” service delivery to producemanagement insights that can be embodied as software decision supportsystems and consulting methodologies.

Derivative instruments have consistently proven their value as a meansfor managing risk (see, e.g. Crouhy, M., D. Galai, and R. Mark. 2001.Risk Management. McGraw-Hill Companies, Inc.), and financial futures andoptions are actively traded on many exchanges. Derivatives are routinelyused to manage financial risks, e.g., exposure to security pricefluctuations, foreign exchange rate movements, and changes in interestrates (Hull, J. 1997. Options, Futures and Other Derivatives.Prentice-Hall, Inc.). Within a more limited scope, a few industries havealso used derivatives to manage risk, see, e.g., Pilipovic, D. 1998(Energy Risk. McGraw-Hill Companies, Inc.) on the use of options inenergy markets, Bassok, Y., R. Sirnivasan, A. Bixby, and H. Wiesel. 1997(“Design of component supply contracts with commitment revisionflexibility”. IBM Journal of Research and Development, Vol. 41, No. 6)on the practice at IBM printer division.

Supply contract terms and conditions often have characteristics thatmake them behave much like financial derivatives. Option-like contractarrangements explored in the literature include buy back policies(Pasternack 1985, Emmons and Gilbert 1998), backup agreements (Eppen andIyer 1997), pay-to-delay capacity reservation (Brown and Lee 1998), andquantity flexibility (Tsay 1999, Tsay and Lovejoy 1999). Barnes-Schusteret al. (2002) explored the impact of contractual real options in abuyer-supplier system

Viewing “On-Demand” as a risk hedging tool, we apply risk managementtechniques to: develop business insights to help suppliers or buyers foron-demand decisions, provide objective assessments to justify the valueof “On-Demand” to a particular firm, and codify a decision makingframework to guide suppliers (or buyers) in making a broad range ofon-demand decisions.

This risk management framework for “On-Demand” services provides afoundation for management tools that are robust and mature due to theirroots in the financial services industry. The framework is transparentand understandable to both service providers and distributors, reducingambiguities associated with “On-Demand” service management.

Simply stated, “On-Demand” is a solution for an enterprise or otherentity to manage the fluctuations and uncertainties in its customerdemand, market opportunities and/or threats, and/or businessenvironment. Based on concepts and theories developed in the financialservices industry for risk management, this invention develops an optionframework to analyze “On-Demand”, thus providing business insights andprinciples to help decisions regarding “On-Demand”. It also providesmodels and tools to evaluate the value of “On-Demand” to both serviceproviders and buyers, by quantifying the impact of “On-Demand” onprofits, cost and revenues. A preferred embodiment of the framework ispresented.

When suppliers provide “On-Demand” offerings, they are selling “options”to buyers; and “On-Demand” is a means for buyers to hedge against risksand uncertainties in their IT and technology needs. Following thefinancial services industry this invention provides an option frameworkand a few models for both suppliers and buyers for sound decision makingwith respect to on-demand. The invention can be implemented as software,tools, and/or consulting methodologies.

Further benefits and advantages of the invention will become apparentfrom a consideration of the following detailed description, given withreference to the accompanying drawings, which specify and show preferredembodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the Service Delivery Chain.

FIG. 2 illustrates a three-step algorithm for service delivery chainmanagement.

FIG. 3 shows the net profit improvement that can be obtained fromservice delivery options.

FIG. 4 shows a computer processing system that may be used to carry outthe present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

As shown in FIG. 1, a Service Delivery Chain 10 can be defined as theset of entities needed to deliver a specific service to a set ofrecipients. Canonically, the Service Delivery Chain 10 is comprised ofthree members: service provider 12, service distributor and end users16. The service provider is the entity that provides the neededresources for an acceptable service level. The service distributor isone who acts as an intermediary between the service provider and the enduser. The end users are those who consume, redistribute, transform, orotherwise utilize the service.

Each entity in the Service Delivery Chain 10 must make managementdecisions to maximize its own profit. The end users 16 choose how muchof the service to consume from the service distributor to satisfy a needor maximize their personal utility. The service distributor 14 mustdecide how many resources to buy from the service provider; consideringuncertain demand from the end users and the resource price structurepresented by the service provider. While the service provider 12 mustdecide a resource price structure and a capacity level to support theneeds of multiple service distributors. This collection of decisionsconstitutes service delivery chain management. One instance of a riskmanagement framework is presented to address service delivery chainmanagement.

This framework is comprised of three components: (1) Provisioning forOn-Demand Services, (2) Pricing for On-Demand Services, and (3) ManagingOn-Demand customer Portfolio. Each of these components is discussedbelow.

The Role of Provisioning for On-Demand Service

The profitability of On-Demand offerings depends on solving thisprovisioning problem. The demand behavior of On-Demand customers isinherently a function of time. That demand can vary, for example,because of the seasonality of events and due to the occurrence ofspecial events. As a result, needed resources vary with time. Importantobjectives are to minimize the cost of over Provisioning and to minimizethe cost of under Provisioning.

The Role of Pricing for On-Demand Services

In order to determine the appropriate pricing, the value of theOn-Demand feature needs to be determined. An options framework givesinsight into pricing. Also, it may be appropriate to price multipleinvocations of the on-demand feature. For instance, consideration may begiven to a call/put pair. The appropriate price/risk relationship mayalso be determined.

Managing On-Demand Customer Portfolio

A number of factors may also be taken into account when managing theOn-Demand Customer Portfolio. One consideration is that customers withhighly correlated demands are risky and less desirable for acceptanceinto the portfolio. Also, it may be desirable to accept customers whosedemand profile contributes to resource pooling.

The supplier must decide the optimal number of options to offer themarket. Offering an infinite number of options is not feasible. This isbecause the supplier's resources are constrained.

Before discussing the methodology of the invention, it may be helpful toconsider an example of an on-demand data center where the invention canbe applied.

As an example, the present invention can be applied with IT/Web Hosting.IT/Web Hosting needs are volatile and hard to predict. A customer caneither buy IT resources directly, or purchase options which give thecustomer the right to buy IT resources after their IT requirement hasbeen observed.

This example involves the following parameters:

□D: the customer's stochastic IT requirement with pdf f(D) and cdf F(D)

□W: traditional purchase price=unit cost of firm order

□C: unit cost of option, X: option exercise price

□R: unit value of IT resource to the customer, M: unit cost to theservice provider

□Q: customer firm order of the IT resource

□q: customer order of the IT resource as option

Also, the sequence of events is: First, the service provider announces(W,C,x) to customers; Second, at t=0, the customer places orders Q andq, the service provider decides IT/Hosting capacity Y, and the serviceprovider delivers Q units, and holds (Y-Q) in inventory; and Third,during the provision of on-demand, demand D is observed, the customerexercises options (q), and the service provider delivers additional ITresources to the customer.

A three-step procedure is presented to address the key features ofService Delivery Chain management. FIG. 2 shows the algorithmpictorially.

At the first step in this process, the service provider 12 announcesupfront capacity pricing, an “On-Demand” premium structure, and an“On-Demand” exercise structure.

The service provider presents a three part pricing structure to theservice distributors 14. The first component of the price is a pricestructure for capacity or resources to be purchased for immediate use.The second component of the price introduces the risk management conceptof an option premium. This second component of the price is an“On-Demand” premium structure. This represents the immediate cost to theservice distributor for the right to use the “On-Demand” feature at somepoint any time in the future up to a specified date. The final componentof the price is an “On-Demand” usage fee structure. This represents theprice the service provider charges a service distributor upon theinvocation of the “On-Demand” feature.

A service level agreement (SLA) may also be reached between the providerand distributor to guarantee that the provider dedicates adequateresources, or otherwise commits to certain terms and conditions definingthe level of service to be provided. Optionally, an agreed upon penaltystructure is developed for a broken SLA.

The service provider should preferably take into account the followingstrategic concerns when establishing the three-part price structure:pricing of competitive offerings, forecasts of service and equipmentcosts, and expected response of distributors to price structure.Competition may drive pricing and or a desire to gain market share maydictate prices. The fact that capacity and equipment costs maydepreciate rapidly due to technological innovation during the lifetimeof an “On-demand” service agreement, suggests that the “On-Demand”premium and exercise prices should reflect this fact. The serviceprovider expected profit could be maximized by selecting a pricingstructure given estimates of the service distributors response to theprice structure.

One way to compute a lower bound for “On-Demand” the option premium, is

c≧E[e ^(−rτ)·((x−m)−τ·s−a _(τ))]

where τ is the random time when a service distributor invokes one unitof the “On-Demand” option, r is the discount rate, x is the “On-Demand”exercise price, m is the cost to the service provider for one unit ofcapacity, s is the maintenance cost per unit time per unit capacityincurred by the provider and a is the cost of activating the “On-Demand”cost incurred by the provider.

At the second step in the process, the service distributors commit toupfront capacity and units of the “on-Demand” option.

In response to end user demand, the service provider's three-part pricestructure and any service level agreements, the service distributorreserves upfront capacity. Further, they decide how many units of“On-Demand” options to purchase.

The service provider should consider the following strategic concernswhen making their commitments: purchasing decisions of other servicedistributors, forecasts of technology change over the contract timeframe, forecast of business need changes and changes in end usersquality of service expectations.

One instance of a service distributor's problem is modeled below. Thegoal is to maximize the service distributors expected profit. Let Q bethe amount of upfront capacity and q be the amount of “On-Demand”options. Then for convenience, we let O=Q+q. The distributor's problemis then:

$\underset{({Q,q})}{Max}{\Pi_{2}\left( {Q,q} \right)}$ where${\Pi_{2}\left( {Q,q} \right)} = {E\begin{bmatrix}{{{r \cdot \min}\mspace{11mu} \left( {D,O} \right)} - {w \cdot Q} -} \\{{c \cdot q} - {{x \cdot \min}\mspace{11mu} \left( {q,\left( {D - Q} \right)^{+}} \right)}}\end{bmatrix}}$

and D is the random end user demand; r is the distributor's revenue perend customer demand satisfied. The optimal amount of upfront capacityand “On-Demand” options

$O^{*} = {F^{- 1}\left( \frac{r - x - c}{r - x} \right)}$

and the number amount of upfront capacity is

$Q^{*} = {{F^{- 1}\left( \frac{x + c - w}{w} \right)}.}$

where F is the estimated cumulative distribution function for the randomdemand D.

At the third step in the process, the service provider provisionscapacity.

The service provider must decide how many resources to dedicate to thecollection of service distributors.

An instance of a service provider's problem is to maximize exceptedprofit given the orders from N service distributors. Formally stated,the problem is

$\mspace{20mu} {\underset{0 \leq \theta \leq 1}{Max}{\Pi_{3}(\theta)}}$  where${\Pi_{3}(\theta)} = {{w{\sum\limits_{i = 1}^{N}Q_{i}^{*}}} + {c{\sum\limits_{i = 1}^{N}q_{i}^{*}}} - {m \cdot {Y(\theta)}} + {E\begin{bmatrix}{{{x \cdot \min}\mspace{11mu} \left( {{\sum\limits_{i = 1}^{N}q_{i}^{*}},{\sum\limits_{i = 1}^{N}\left( {D_{i} - Q_{i}^{*}} \right)^{+}}} \right)} +} \\{{s \cdot {\sum\limits_{i = 1}^{N}\left( {Q_{i}^{*} + {\theta \; q_{i}^{*}} - {\min \; \left( {D_{i},O_{i}^{*}} \right)}} \right)^{+}}} +} \\{p \cdot {\sum\limits_{i = 1}^{N}\left( {{\min \mspace{11mu} \left( {D_{i},O_{i}^{*}} \right)} - Q_{i}^{*} - {\theta \; q_{i}^{*}}} \right)^{+}}}\end{bmatrix}}}$   and$\mspace{20mu} {{Y(\theta)} = {{\sum\limits_{i = 1}^{N}Q_{i}^{*}} + {\theta \cdot {\sum\limits_{i = 1}^{N}{q_{i}^{*}.}}}}}$

The expectation is taken over the joint distribution of all the randomdemands. If all the demands are independent then no resource poolingexists and the optimization problem decouples into N independentproblems. The decision variable □ represents a measure of resourcepooling intensity for a given collection of service distributions. Aservice provider should accept customers whose demand profilecontributes to resource pooling. A candidate service distributor shouldbe considered for inclusion into the provider's portfolio if theexpected profits increase when included, and referred otherwise.

Consider the two following examples:

1. A service provider offers high-end servers (with price at about $1million) on-demand. In this scenario, the service provider puts X numberof processors in the box, the buyer initially only pays for Y (<X)processors, and they pay for the rest of (X-Y) the processors when theprocessors are actually used.

2. In a Grid computing environment, or in other multi-computerenvironments, a service provider may dedicate certain computationalcapacity to customers, and customers only pay the service provider whenthey actually use the service provider's service.

In both of these two examples, the service provider is actuallyproviding customers with an option (of using up to X-Y processors in thefirst example, and of utilizing the provider's computational service inthe second example). In accordance with the present invention, on-demandoptions can (when appropriately priced and utilized):

1. Provide a mechanism for the service provider and its customers toshare the risk (of uncertainty and fluctuation in itsIT/technology/computational needs).

2. Enables both the service provider and its customer's higher profitsor lower cost (in comparison with the traditional own-and-operatemodel).

3. Encourage customers to share their technology needs information withthe service provider, and therefore improve the service provider'splanning and help increase that provider's market share by locking-inthe customers' IT demand.

Options as a mechanism for risk sharing is a win-win strategy for boththe Retailer and the Supplier. As FIG. 3 illustrates, both the supplierand the retailer can show significant net profit improvement fromservice delivery options.

The use of Options Framework for On-Demand Services provides a number ofother benefits as well. These other benefits include customerflexibility, a mechanism for risk sharing, and a means to enhanceinformation flows. Also, this Options Framework provides a trustedfoundation for pricing.

As will be readily apparent to those skilled in the art, the presentinvention can be realized in hardware, software, or a combination ofhardware and software. Any kind of computer/server system(s)—or otherapparatus adapted for carrying out the methods described herein—issuited. A typical combination of hardware and software could be ageneral-purpose computer system with a computer program that, whenloaded and executed, carries out the respective methods describedherein. Alternatively, a specific use computer, containing specializedhardware for carrying out one or more of the functional tasks of theinvention, could be utilized.

The present invention, or aspects of the invention, can also be embodiedin a computer program product, which comprises all the respectivefeatures enabling the implementation of the methods described herein,and which—when loaded in a computer system—is able to carry out thesemethods. Computer program, software program, program, or software, inthe present context mean any expression, in any language, code ornotation, of a set of instructions intended to cause a system having aninformation processing capability to perform a particular functioneither directly or after either or both of the following: (a) conversionto another language, code or notation; and/or (b) reproduction in adifferent material form.

The present invention, or features of the invention, may be generallyimplemented by a data processing system, and FIG. 4 is a pictorialrepresentation of a distributed data processing system in which thepresent invention may be implemented. System 100 is a network ofcomputers and utilizes a network 102, which is the medium used toprovide communications links between various devices and computersconnected together within distributed data processing system 100.Network 102 may include permanent connections, such as wire or fiberoptic cables, or temporary connections made through telephoneconnections.

In the depicted example, server 104 is connected to network 102, alongwith storage unit 106. In addition, clients 108, 110 and 112 are alsoconnected to network 102. These clients 108, 110 and 112 may be, forexample, personal computers or network computers. In the implementationof the present invention, one of these clients may represent the serviceprovider and others the clients may represent the service distributors.In the depicted example, server 104 provides data, such as boot files,operating system images, and applications, to clients 108, 110 and 112.Distributed data processing system 100 may include additional servers,clients, and other devices not shown. In the depicted example, network102 is the Internet, representing a worldwide collection of networks andgateways that use the TCP/IP suite of protocols to communicate with oneanother.

At the heart of the Internet is a backbone of high-speed datacommunication lines between major nodes or host computers consisting ofthousands of commercial, government, education, and other computersystems that route data and messages. Of course, distributed dataprocessing system 100 also may be implemented as a number of differenttypes of networks, such as, for example, an intranet, a local areanetwork (LAN), or a wide area network (WAN). FIG. 4 is intended as anexample and not as an architectural limitation for the presentinvention.

While it is apparent that the invention herein disclosed is wellcalculated to fulfill the objects stated above, it will be appreciatedthat numerous modifications and embodiments may be devised by thoseskilled in the art, and it is intended that the appended claims coverall such modifications and embodiments as fall within the true spiritand scope of the present invention.

1. A method of managing on-demand service offerings, wherein a serviceprovider provides resources to a collection of service distributors, andsaid service distributors distribute said resources to end users, themethod comprising the steps of: the service provider announcing upfrontcapacity pricing, an on-demand premium structure, and an on-demandexercise structure; at least one of the service distributors committingto upfront capacity and to units of on-demand options; and the serviceprovider provisioning a number of resources to the collection of servicedistributors with the goal of maximizing the expected profit based onthe orders and options.
 2. A method according to claim 1, wherein theprovisioning step is done with the goal of maximizing the expectedprofit based on the orders and options.
 3. A method according to claim1, wherein the pricing includes three components: a first component is aprice structure for capacity or resources to be purchased for immediateuse; a second component is an on-demand premium structure; and a thirdcomponent is an on-demand usage fee structure.
 4. A method according toclaim 3, wherein said on-demand premium structure represents animmediate cost to the service distributor for the right to use theon-demand feature at some point any time in the future up to a specifieddate.
 5. A method according to claim 3, wherein said on-demand usage feestructure represents the price the service provider charges a servicedistributor upon invocation of the on-demand feature.
 6. A methodaccording to claim 3, wherein said price structure for capacity isselected based on given estimates of the service distributor's responseto the price structure.
 7. A method according to claim 1, wherein saidannouncing upfront capacity is based on pricing of competitiveofferings, forecasts of service and equipment costs, and expectedresponse of distributors to price structure.
 8. A method according toclaim 1, wherein said committing to upfront capacity is based on atleast one of: purchasing decisions of other service distributors,forecasts of technology change, forecasts of business need change, andchanges in end users quality of service expectation.
 9. A methodaccording to claim 1, comprising the further step of reaching anagreement between the service provider and said one of the servicedistributors to guarantee that the service provider dedicates adequateresources.
 10. A system for managing on-demand service offerings,wherein a service provider provides resources to a collection of servicedistributors, and said service distributors distribute said resources toend users, said system comprising: a memory device having embodiedtherein information relating to said resources; a service providerprocessor in communication with said memory device and configured forannouncing upfront capacity pricing, an on-demand premium structure, andan on-demand exercise structure; and a service distributor processor incommunication with said service provider processor and configured forcommitting to upfront capacity and to units of on-demand options;wherein said service provider processor is further configured forprovisioning a number of resources to the collection of servicedistributors.
 11. A system according to claim 10, wherein the upfrontcapacity pricing includes three components: a first component is a pricestructure for capacity or resources to be purchased for immediate use; asecond component is an on-demand premium structure; and a thirdcomponent is an on-demand usage fee structure.
 12. A system according toclaim 10, wherein the committing to upfront capacity and to units ofon-demand options is done by using the equation: whereΠ₂(Q,q)=E[r·min(D,O)−w·Q−c·q−x·min(q,(D−Q)⁺)] where D is the random enduser demand; r is the distributor's revenue per end customer demandsatisfied. Note that the randomness D captures the demand risk. It canalso be used to model the technology and market risk. The optimal amountof upfront capacity and “On-Demand” options$O^{*} = {F^{- 1}\left( \frac{r - x - c}{r - x} \right)}$ and thenumber amount of upfront capacity is$Q^{*} = {{F^{- 1}\left( \frac{x + c - w}{w} \right)}.}$ where F is theestimated cumulative distribution function for the random demand D. 13.A system according to claim 10, wherein the provisioning of done byusing the equation:${\Pi_{3}(\theta)} = {{w{\sum\limits_{i = 1}^{N}Q_{i}^{*}}} + {c{\sum\limits_{i = 1}^{N}q_{i}^{*}}} - {m \cdot {Y(\theta)}} + {E\begin{bmatrix}{{{x \cdot \min}\mspace{11mu} \left( {{\sum\limits_{i = 1}^{N}q_{i}^{*}},{\sum\limits_{i = 1}^{N}\left( {D_{i} - Q_{i}^{*}} \right)^{+}}} \right)} +} \\{{s \cdot {\sum\limits_{i = 1}^{N}\left( {Q_{i}^{*} + {\theta \; q_{i}^{*}} - {\min \; \left( {D_{i},O_{i}^{*}} \right)}} \right)^{+}}} +} \\{p \cdot {\sum\limits_{i = 1}^{N}\left( {{\min \mspace{11mu} \left( {D_{i},O_{i}^{*}} \right)} - Q_{i}^{*} - {\theta \; q_{i}^{*}}} \right)^{+}}}\end{bmatrix}}}$   where$\mspace{20mu} {{Y(\theta)} = {{\sum\limits_{i = 1}^{N}Q_{i}^{*}} + {\theta \cdot {\sum\limits_{i = 1}^{N}{q_{i}^{*}.}}}}}$14. A system according to claim 10, wherein said on-demand premiumstructure represents a cost to the service distributor for the right touse the on-demand feature at some point any time in the future.
 15. Asystem according to claim 10, wherein said on-demand usage fee structurerepresents the price the service provider charges a service distributorfor invocation of the on-demand feature.
 16. A program storage devicereadable by machine, tangibly embodying a program of instructionsexecutable by the machine to perform a method of managing on-demandservice offerings, wherein a service provider provides resources to agroup of service distributors, and said service distributors distributesaid resources to end users, the method comprising the steps of: theservice provider announcing upfront capacity pricing, an on-demandpremium structure, and an on-demand exercise structure; at least one ofthe service distributors committing to upfront capacity and to units ofon-demand options; and the service provider provisioning a number ofresources to the collection of service distributors.
 17. A programstorage device according to claim 16, wherein: the upfront capacitypricing includes three components: a first component is a pricestructure for capacity or resources to be purchased for immediate use, asecond component is an on-demand premium structure, and a thirdcomponent is an on-demand usage fee structure; said on-demand premiumstructure represents an immediate cost to the service distributor forthe right to use the on-demand feature at some point any time in thefuture; and said on-demand usage fee structure represents the price theservice provider charges a service distributor for invocation of theon-demand feature.
 18. A program storage device according to claim 17,wherein: the committing to upfront capacity and to units of on-demandoptions is done by using the equation:$\underset{({Q,q})}{Max}{\Pi_{2}\left( {Q,q} \right)}$ where${\Pi_{2}\left( {Q,q} \right)} = {E\begin{bmatrix}{{{r \cdot \min}\mspace{11mu} \left( {D,O} \right)} - {w \cdot Q} -} \\{{c \cdot q} - {{x \cdot \min}\mspace{11mu} \left( {q,\left( {D - Q} \right)^{+}} \right)}}\end{bmatrix}}$ and D is the random end user demand; r is thedistributor's revenue per end customer demand satisfied. The optimalamount of upfront capacity and “On-Demand” options$O^{*} = {F^{- 1}\left( \frac{r - x - c}{r - x} \right)}$ and thenumber amount of upfront capacity is$Q^{*} = {{F^{- 1}\left( \frac{x + c - w}{w} \right)}.}$ where F is theestimated cumulative distribution function for the random demand D; andthe provisioning of done by using the equation:$\mspace{20mu} {\underset{0 \leq \theta \leq 1}{Max}{\Pi_{3}(\theta)}}$${\Pi_{3}(\theta)} = {{w{\sum\limits_{i = 1}^{N}Q_{i}^{*}}} + {c{\sum\limits_{i = 1}^{N}q_{i}^{*}}} - {m \cdot {Y(\theta)}} + {E\begin{bmatrix}{{{x \cdot \min}\mspace{11mu} \left( {{\sum\limits_{i = 1}^{N}q_{i}^{*}},{\sum\limits_{i = 1}^{N}\left( {D_{i} - Q_{i}^{*}} \right)^{+}}} \right)} +} \\{{s \cdot {\sum\limits_{i = 1}^{N}\left( {Q_{i}^{*} + {\theta \; q_{i}^{*}} - {\min \; \left( {D_{i},O_{i}^{*}} \right)}} \right)^{+}}} +} \\{p \cdot {\sum\limits_{i = 1}^{N}\left( {{\min \mspace{11mu} \left( {D_{i},O_{i}^{*}} \right)} - Q_{i}^{*} - {\theta \; q_{i}^{*}}} \right)^{+}}}\end{bmatrix}}}$   where$\mspace{20mu} {{Y(\theta)} = {{\sum\limits_{i = 1}^{N}Q_{i}^{*}} + {\theta \cdot {\sum\limits_{i = 1}^{N}{q_{i}^{*}.}}}}}$